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REVIEW 4 major objections 5 minor 1 cited by

Fine-tuning for emotion recognition yields state-of-the-art detection of uncompressed audio deepfakes.

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EmoAnti fine-tunes Wav2Vec2 on emotion recognition and refines the resulting features with a convolutional residual extractor, achieving low EER on ASVspoof LA but worse performance on DF than its own no-finetuning baseline.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection Genuinely good LA results and a plausible emotion-transfer idea, but the abstract overclaims on ASVspoof2021DF and the paper's own ablation shows the emotion-guided components hurt compressed speech. the 4 major comments →

arxiv 2509.10781 v1 pith:NUWTWJOW submitted 2025-09-13 cs.SD

Emoanti: audio anti-deepfake with refined emotion-guided representations

Xiaokang Li , Yicheng Gong , Dinghao Zou , Xin Cao , Sunbowen Lee This is my paper
classification cs.SD
keywords audio anti-deepfakeemotion-guided representationsspeech emotion recognitionWav2Vec2 fine-tuningconvolutional residual feature extractorspoofing countermeasureslogical-access deepfake detection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that high-level emotional cues are a useful, underused signal for detecting audio deepfakes. It fine-tunes a pretrained speech model (Wav2Vec2) on emotion recognition, then routes the resulting emotion-guided representations through a convolutional residual feature extractor before classification. On uncompressed logical-access benchmarks, error rates drop to 0.44% and 4.62%, well below the comparison systems. The paper's own ablation shows the emotional components hurt on a compressed deepfake benchmark, and the authors attribute this to overfitting or to the extractor losing global context. If the transfer problem is solved, the approach suggests that anti-deepfake systems should exploit semantic cues like emotion, not just low-level acoustics.

Core claim

The paper claims that fine-tuning Wav2Vec2 on a four-category emotion task produces emotion-guided representations, and refining outputs from multiple transformer layers with a convolutional residual feature extractor makes bonafide versus spoof speech easier to tell apart. On the 2019 logical-access benchmark the system reaches 0.44% equal error rate and 0.0139 minimum t-DCF; on the 2021 logical-access benchmark it reaches 4.62% EER and 0.292 min t-DCF. The reported ablation shows the full system is worse on the compressed deepfake set (13.72% EER) than a version with neither emotion fine-tuning nor the extractor (11.46% EER), which the authors explain as overfitting to logical-access data

What carries the argument

Wav2Vec2-large-robust, a pretrained transformer for speech, is fine-tuned to classify four emotions (angry, happy, sad, neutral). Its per-layer transformer outputs feed a convolutional residual feature extractor (CRFE): four blocks, each with two 1D convolutions and a residual skip connection, followed by a temporal attention fusion module that weights each time step and concatenates the refined vectors. The classifier is a two-layer fully connected network. The CRFE does the work of turning emotion-tuned representations into a discriminative signal for emotional discrepancies between real and synthetic speech.

Load-bearing premise

That emotion categories learned on a small acted emotional-speech corpus transfer to the spoof-versus-bonafide differences in the evaluation audio, including compressed deepfakes—a transfer the ablation shows failing on compressed audio.

What would settle it

Check the ablation in the paper: on the compressed deepfake benchmark, the full EmoAnti (13.72% EER) is worse than the variant without emotion fine-tuning and without the convolutional extractor (11.46% EER). Re-running this ablation on the same data would settle whether emotion-guided components help generalise to compressed speech.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Anti-deepfake systems can be built on semantic cues such as emotion rather than only on low-level acoustic statistics, which often generalise poorly.
  • Fine-tuning a pretrained speech model on emotion recognition is a data-efficient route to more interpretable spoof-detection features on clear audio.
  • The reported error rates on uncompressed logical-access audio suggest the method could be practical for synthetic-voice screening in clean recordings.
  • The degraded performance on compressed audio means deployment on telephone or codec-transmitted speech requires adapting the emotional features or adding global context.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The emotion-transfer assumption probably fails on compressed audio because codecs erase the subtle prosodic cues that emotion fine-tuning emphasises; a testable fix is to fine-tune the emotion head on compressed recordings.
  • The CRFE's local convolutions may be the culprit on deepfake sets; a global-attention or hierarchical pooling replacement could keep the logical-access gains while recovering robustness.
  • The close numbers on logical-access benchmarks may overstate the method's field readiness, since the evaluation sets share attack algorithms with training; cross-dataset and real-world recordings are the next test.
  • A natural extension is to treat emotion recognition as an auxiliary task during spoof detection training, rather than a separate fine-tuning step, to force the model to keep emotion cues while learning to spot fakes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes EmoAnti, an audio anti-deepfake system built from a Wav2Vec2-large-robust backbone fine-tuned on the IEMOCAP emotion recognition task (four emotion classes), followed by a convolutional residual feature extractor (CRFE) that refines representations from multiple transformer layers, a temporal-attention feature fusion module, and a two-layer classifier. The method is evaluated on ASVspoof2019 LA, ASVspoof2021 LA, and ASVspoof2021 DF, reporting EERs of 0.44%, 4.62%, and 13.72%, respectively, together with an ablation that removes emotion fine-tuning and/or CRFE. The paper claims state-of-the-art performance on both LA benchmarks and strong generalization to ASVspoof2021DF.

Significance. If the LA results are reproducible, the paper makes a useful contribution: it shows that emotion-guided fine-tuning of a self-supervised speech model can yield large gains over vanilla W2V2 on uncompressed logical-access spoofing benchmarks (0.44% vs. 1.47–2.31% EER on 2019LA, and 4.62% vs. 7.62–10.97% on 2021LA). The architecture is reasonably novel, and the use of standard public benchmarks plus the availability of code are strengths. However, the headline claim of 'strong generalization' on ASVspoof2021DF is directly contradicted by the paper's own tables and ablations: on DF, EmoAnti is worse than several baselines and worse than its own non-emotion ablated variant. The contribution is therefore narrower than claimed, and the paper needs substantial revision before it can be accepted.

major comments (4)
  1. [Abstract; §4.2; Table 2; Fig. 3] The abstract's claim of 'strong generalization on the ASVspoof2021DF dataset' is not supported by the reported numbers. On 2021DF, EmoAnti achieves 13.72% EER, while the W2V2+LCNN+BLSTM baselines achieve 5.44% and 7.14% (Table 2). Moreover, Fig. 3 shows that the plain W2V2(unft) without emotion fine-tuning and without CRFE achieves 11.46% EER on DF, which is better than the full EmoAnti (13.72%). Each added component individually hurts DF performance: W2V2(unft)+CRFE gives 16.14% and W2V2(ft) gives 12.57%. Thus the emotion-guided components worsen, rather than improve, generalization on compressed/transcoded speech. The 'strong generalization' sentence in the abstract and the corresponding claim in §4.2 must be removed or substantially weakened.
  2. [§4.3 Ablation Study] The interpretation of the ablation is internally inconsistent for the DF condition. The text states that 'the coupling of emotion-aware fine-tuning and the CRFE is highly effective' and that 'removing both fine-tuning and the CRFE results in the best performance' on DF. The full model's 13.72% is worse than the no-fine-tuning/no-CRFE variant's 11.46%, so the coupling is not effective on DF. The concession in Section 5 that CRFE needs to be replaced to improve 2021DF generalization only reinforces this. The conclusions should be reframed to separate the LA results, where the components help, from the DF results, where they do not.
  3. [§3.2; Tables 1–2] No measure of variability is reported, although the training setup describes three dataset-specific seeds. The EER and min t-DCF values are given as single numbers, which is insufficient to support state-of-the-art claims when differences are small (e.g., 0.44% vs. 1.47% on 2019LA). Please report mean±std over the three seeds, or at least provide seed-wise results and a statistical comparison.
  4. [§2.1; §3.1; Fig. 3] The core assumption that emotion categories learned on acted IEMOCAP speech transfer to the bonafide/spoof distinction in ASVspoof is not directly examined. The ablation results suggest the assumption fails for 2021DF: emotion fine-tuning increases DF EER relative to unfine-tuned W2V2 (12.57% vs. 11.46%). The paper should provide evidence about what the emotion-guided representations actually encode on spoofed vs. bonafide audio (e.g., emotion classification accuracy on ASVspoof utterances, per-emotion analysis, or feature visualizations), or explicitly limit the claim to the LA conditions where the components help.
minor comments (5)
  1. [Abstract] Typographical errors: 'novelaudio', 'utilizeemotional', and 'demonstrating strong generalization' (should be 'demonstrates'). The abstract also needs a more careful phrasing of the DF claim (see major comment).
  2. [§4.2] The sentence 'outperforming most models' on 2021DF is misleading: EmoAnti outperforms only the handcrafted-feature baselines, while the W2V2+LCNN+BLSTM baselines are substantially better. Also, no min t-DCF is reported for DF, making the LA/DF comparison incomplete.
  3. [Tables 1–2] The baseline set is small and mostly from 2021 or earlier; recent self-supervised countermeasures such as WavLM-based systems are not compared. To support 'state-of-the-art' claims, the comparison should include a broader set of published systems.
  4. [Fig. 3] The bar chart has no error bars, and the color/ordering of the four methods is not described in the caption. Adding error bars or seed-wise markers would help interpret the 2019LA and 2021LA differences.
  5. [Section 5] The conclusion's statement that 'the CRFE needs replacing' is in tension with the earlier claim of effectiveness; the revision should make the scope of the method's applicability explicit.

Circularity Check

0 steps flagged

No circularity: held-out benchmark evaluation, no fitted-input-as-prediction, no load-bearing self-citation.

full rationale

The derivation chain is entirely empirical: the paper fine-tunes Wav2Vec2 on IEMOCAP emotion labels (Sections 2.1 and 3.1), then trains a lightweight classifier on ASVspoof2019 LA and tests on held-out ASVspoof2019LA, 2021LA, and 2021DF evaluation sets. The emotion-trained representations are inputs, but the anti-spoofing labels and test sets are disjoint from the emotion fine-tuning data, so no prediction is equivalent to an input by construction. The paper does not fit a parameter to the test set and call it prediction; the ablations in Fig. 3 are genuine comparisons. There are no self-citations, no imported uniqueness theorem, and no renamed known result. The only noteworthy issue is internal consistency: the abstract claims 'strong generalization on ASVspoof2021DF' while the ablation shows the full model (13.72%) underperforms the plain W2V2(unft) baseline (11.46%) on that set, and Section 5 concedes the CRFE needs replacement. This is a correctness/consistency weakness, not a circularity: the failure is empirical and falsifiable, and it does not reduce a claimed result to its own assumptions. Hence the step list is empty and the circularity score is 0.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The method does not introduce new physical entities or mathematical axioms. It relies on standard transfer-learning assumptions, chiefly that emotion supervision on acted speech is a useful inductive bias for spoof detection. The only ad hoc element is the assumption that emotional differences exist between bonafide and spoofed audio, which is not tested directly.

free parameters (4)
  • Emotion fine-tuning learning rate = 1e-5
    Chosen by validation; part of the proposed pipeline.
  • Anti-deepfake learning rate = 1e-4
    Chosen by validation.
  • Number of CRFE blocks = 4
    Design choice; no sensitivity analysis.
  • Transformer layers fed to CRFE
    Not specified in the paper; selection of which layers are aggregated is unstated.
axioms (4)
  • domain assumption Emotion recognition supervision on IEMOCAP transfers useful cues to spoofing detection.
    The entire method relies on this; entered in Section 2.1.
  • domain assumption Wav2Vec2-large-robust representations are a valid frontend for anti-spoofing.
    Used as backbone; supported by prior work but assumed here.
  • domain assumption ASVspoof2019 training generalizes to ASVspoof2021 conditions.
    Standard challenge protocol, but the DF results indicate it fails for compression.
  • ad hoc to paper Spoofed audio differs from bonafide in emotional expression.
    Proposed as motivation; not directly verified with emotion labels on spoof data.

reviewed 2026-08-04 · how reviews work

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Cite this review

Pith. "Pith review of Emoanti: audio anti-deepfake with refined emotion-guided representations." pith.science (2026). https://pith.science/paper/NUWTWJOW

@misc{pith2026250910781,
  author       = {Pith},
  title        = {Pith review of: Emoanti: audio anti-deepfake with refined emotion-guided representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NUWTWJOW}},
  note         = {Machine review of arXiv:2509.10781}
}
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read the original abstract

Audio deepfake is so sophisticated that the lack of effective detection methods is fatal. While most detection systems primarily rely on low-level acoustic features or pretrained speech representations, they frequently neglect high-level emotional cues, which can offer complementary and potentially anti-deepfake information to enhance generalization. In this work, we propose a novel audio anti-deepfake system that utilizes emotional features (EmoAnti) by exploiting a pretrained Wav2Vec2 (W2V2) model fine-tuned on emotion recognition tasks, which derives emotion-guided representations, then designing a dedicated feature extractor based on convolutional layers with residual connections to effectively capture and refine emotional characteristics from the transformer layers outputs. Experimental results show that our proposed architecture achieves state-of-the-art performance on both the ASVspoof2019LA and ASVspoof2021LA benchmarks, and demonstrates strong generalization on the ASVspoof2021DF dataset. Our proposed approach's code is available at Anonymous GitHub1.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks

    eess.AS 2026-04 unverdicted novelty 6.0

    ProSDD learns speaker-conditioned prosodic variation from real speech via supervised masked prediction and jointly optimizes it with spoof detection, cutting EER substantially on ASVspoof 2024 and emotional datasets.

Reference graph

Works this paper leans on

31 extracted references · 7 linked inside Pith · cited by 1 Pith paper

  1. [1]

    INTRODUCTION Text-to-speech (TTS) and voice conversion (VC) technolo- gies, empowered by the rapid advancement of deep learning, have achieved remarkable realism in synthesized speech gen- eration [1]. While this progress has expanded their applica- tion domains, it also raises significant security concerns: such synthetic speech can be exploited to launc...

  2. [2]

    Based on emotional-aware embeddings, we propose a anti-deepfake method, which achieves strong generalization performance and maintains interpretability

  3. [3]

    To better capture and refine emotion-guided represen- tations in speech, we design a convolutional residual feature extractor built upon Wav2Vec2

  4. [4]

    To validate the importance of emotional information in audio anti-deepfake, we conduct comprehensive ablation studies, which provide new insights for future research. arXiv:2509.10781v1 [cs.SD] 13 Sep 2025 Transformer Emotional Feature Vectors Transformer Transformer fine-tuned Wav2Vec2 Conv1d Encoder x1 x2 xN H1 H2 HL Convolutional Residual Feature Extra...

    Pith/arXiv arXiv 2025

  5. [5]

    PROPOSED METHOD The overall architecture of the proposed model is illustrated in Fig. 1. Given raw audio waveforms as input, the fine-tuned Wav2Vec2 model processes them through a series of Conv1d and transformer layers to extract high-level emotion-guided representations. These features are then passed to the convo- lutional residual feature extractor an...

  6. [6]

    EXPERIMENTS 3.1. Datasets and Evaluation Metrics We fine-tune the model on IEMOCAP [12], following the emotion label selection in prior work [13]: ”angry”, ”happy” (including ”excited”), ”sad”, and ”neutral”, resulting in 5,531 utterances. Performance is evaluated using accuracy and macro F1-score. For anti-deepfake, we evaluate on ASVspoof2019 LA and ASV...

    2021

  7. [7]

    RESULTS AND ANALYSIS 4.1. Results on ASVspoof2019 LA Table 1 compares our method with representative approaches that use either handcrafted low-level acoustic features or self- supervised speech models as frontends. On the ASVspoof2019LA Table 1: Comparison results of EER (%) and min t-DCF be- tween our proposed method and other anti-deepfake systems on t...

    2019

  8. [8]

    CONCLUSION In this paper, we propose EmoAnti for audio anti-deepfake, which is evaluated on three datasets. The main contribution of EmoAnti is constructing a Wav2Vec2 fine-tuned on emotion recognition tasks to extract emotional cues from speech, with subsequent refinement via a convolutional residual feature ex- tractor to distinguish emotional differenc...

  9. [9]

    We thank the reviewers for their contributions

    ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (NO.12171378) and High Performance Computing Center of Wuhan University of Science and Tech- nology. We thank the reviewers for their contributions

  10. [10]

    Audio deepfake detection: A survey,

    J. Yi, C. Wang, J. Tao, X. Zhang, C. Y . Zhang, and Y . Zhao, “Audio deepfake detection: A survey,”arXiv preprint arXiv:2308.14970, 2023

    Pith/arXiv arXiv 2023

  11. [11]

    Audio deepfake detection based on a combination of f0 information and real plus imag- inary spectrogram features,

    J. Xue, C. Fan, Z. Lv, J. Tao, J. Yi, C. Zheng, Z. Wen, M. Yuan, and S. Shao, “Audio deepfake detection based on a combination of f0 information and real plus imag- inary spectrogram features,” inProceedings of the 1st international workshop on deepfake detection for audio multimedia, 2022, pp. 19–26

    2022

  12. [12]

    V oice spoofing countermeasure for logical access at- tacks detection,

    T. Arif, A. Javed, M. Alhameed, F. Jeribi, and A. Tahir, “V oice spoofing countermeasure for logical access at- tacks detection,”IEEE Access, vol. 9, pp. 162857– 162868, 2021

    2021

  13. [13]

    Deepfake speech detection through emotion recogni- tion: a semantic approach,

    E. Conti, D. Salvi, C. Borrelli, B. Hosler, P. Bestagini, F. Antonacci, A. Sarti, M. C. Stamm, and S. Tubaro, “Deepfake speech detection through emotion recogni- tion: a semantic approach,” inICASSP 2022-2022 IEEE international conference on acoustics, speech and sig- nal processing (ICASSP). IEEE, 2022, pp. 8962–8966

    2022

  14. [14]

    The vicomtech audio deepfake detection system based on wav2vec2 for the 2022 add challenge,

    J. M. Mart ´ın-Do˜nas and A. ´Alvarez, “The vicomtech audio deepfake detection system based on wav2vec2 for the 2022 add challenge,” inICASSP 2022-2022 IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 9241–9245

    2022

  15. [15]

    Audio anti-spoofing based on audio feature fusion,

    J. Zhang, G. Tu, S. Liu, and Z. Cai, “Audio anti-spoofing based on audio feature fusion,”Algorithms, vol. 16, no. 7, pp. 317, 2023

    2023

  16. [16]

    Audio deepfake detection with self- supervised wavlm and multi-fusion attentive classifier,

    Yinlin Guo, Haofan Huang, Xi Chen, He Zhao, and Yuehai Wang, “Audio deepfake detection with self- supervised wavlm and multi-fusion attentive classifier,” inICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 12702–12706

    2024

  17. [17]

    Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,

    W.-N. Hsu, A. Sriram, A. Baevski, T. Likhomanenko, Q. Xu, V . Pratap, J. Kahn, A. Lee, R. Collobert, G. Syn- naeve, et al., “Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,”arXiv preprint arXiv:2104.01027, 2021

    Pith/arXiv arXiv 2021

  18. [18]

    Wav2Vec2 - Large - Robust model,

    Facebook AI, “Wav2Vec2 - Large - Robust model,” https://huggingface.co/facebook/ wav2vec2-large-robust

  19. [19]

    Large-scale self-supervised speech representation learning for automatic speaker verification,

    Z. Chen, S. Chen, Y . Wu, Y . Qian, C. Wang, S. Liu, Y . Qian, and M. Zeng, “Large-scale self-supervised speech representation learning for automatic speaker verification,” inICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 6147–6151

    2022

  20. [20]

    Dawn of the transformer era in speech emotion recog- nition: closing the valence gap,

    J. Wagner, A. Triantafyllopoulos, H. Wierstorf, M. Schmitt, F. Burkhardt, F. Eyben, and B. W. Schuller, “Dawn of the transformer era in speech emotion recog- nition: closing the valence gap,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10745–10759, 2023

    2023

  21. [21]

    Iemocap: Interactive emotional dyadic motion capture database,

    C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,”Language resources and evaluation, vol. 42, no. 4, pp. 335–359, 2008

    2008

  22. [22]

    Speech emotion recognition using self-supervised features,

    E. Morais, R. Hoory, W. Zhu, I. Gat, M. Damasceno, and H. Aronowitz, “Speech emotion recognition using self-supervised features,” inICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Sig- nal Processing (ICASSP). IEEE, 2022, pp. 6922–6926

    2022

  23. [23]

    Asvspoof 2019: Future horizons in spoofed and fake audio detection,

    M. Todisco, X. Wang, V . Vestman, M. Sahidullah, H. Delgado, A. Nautsch, J. Yamagishi, N. Evans, T. Kin- nunen, and K. A. Lee, “Asvspoof 2019: Future horizons in spoofed and fake audio detection,”arXiv preprint arXiv:1904.05441, 2019

    Pith/arXiv arXiv 2019

  24. [24]

    Asvspoof 2021: accelerat- ing progress in spoofed and deepfake speech detection,

    J. Yamagishi, X. Wang, M. Todisco, M. Sahidullah, J. Patino, A. Nautsch, X. Liu, K. A. Lee, T. Kin- nunen, N. Evans, et al., “Asvspoof 2021: accelerat- ing progress in spoofed and deepfake speech detection,” arXiv preprint arXiv:2109.00537, 2021

    Pith/arXiv arXiv 2021

  25. [25]

    Asvspoof 2021: Towards spoofed and deepfake speech detection in the wild,

    X. Liu, X. Wang, M. Sahidullah, J. Patino, H. Del- gado, T. Kinnunen, M. Todisco, J. Yamagishi, N. Evans, A. Nautsch, et al., “Asvspoof 2021: Towards spoofed and deepfake speech detection in the wild,”IEEE/ACM Transactions on Audio, Speech, and Language Process- ing, vol. 31, pp. 2507–2522, 2023

    2021

  26. [26]

    t-dcf: a detection cost function for the tandem assessment of spoofing countermeasures and automatic speaker verification,

    T. Kinnunen, K. A. Lee, H. Delgado, N. Evans, M. Todisco, M. Sahidullah, J. Yamagishi, and D. A. Reynolds, “t-dcf: a detection cost function for the tandem assessment of spoofing countermeasures and automatic speaker verification,”arXiv preprint arXiv:1804.09618, 2018

    Pith/arXiv arXiv 2018

  27. [27]

    ASVspoof 2021 Baseline CM & Evaluation Package,

    ASVspoof2021 challenge organizers, “ASVspoof 2021 Baseline CM & Evaluation Package,”https: //github.com/asvspoof-challenge/2021, 2021

    2021

  28. [28]

    Known-unknown data augmentation strate- gies for detection of logical access, physical access and speech deepfake attacks: Asvspoof 2021,

    R. K. Das, “Known-unknown data augmentation strate- gies for detection of logical access, physical access and speech deepfake attacks: Asvspoof 2021,”Proc. 2021 Edition of the Automatic Speaker Verification and Spoof- ing Countermeasures Challenge, pp. 29–36, 2021

    2021

  29. [29]

    End-to-end anti-spoofing with rawnet2,

    H. Tak, J. Patino, M. Todisco, A. Nautsch, N. Evans, and A. Larcher, “End-to-end anti-spoofing with rawnet2,” inICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 6369–6373

    2021

  30. [30]

    Ur channel- robust synthetic speech detection system for asvspoof 2021,

    X. Chen, Y . Zhang, G. Zhu, and Z. Duan, “Ur channel- robust synthetic speech detection system for asvspoof 2021,”arXiv preprint arXiv:2107.12018, 2021

    arXiv 2021

  31. [31]

    Investigating self- supervised front ends for speech spoofing countermea- sures,

    X. Wang and J. Yamagishi, “Investigating self- supervised front ends for speech spoofing countermea- sures,”arXiv preprint arXiv:2111.07725, 2021

    Pith/arXiv arXiv 2021

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.